Multi-Linear Interactive Matrix Factorization
This work addresses the need for more accurate recommendations in online platforms by integrating pairwise interactions, though it appears incremental as it builds on existing matrix factorization approaches.
The paper tackles the problem of improving recommender systems by incorporating additional factors beyond user-item ratings, proposing a multi-linear interactive matrix factorization algorithm that models interactions between users and event-associated factors, and reports better accuracy on MovieLens datasets compared to baseline methods.
Recommender systems, which can significantly help users find their interested items from the information era, has attracted an increasing attention from both the scientific and application society. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of MF based approaches focus on the user-item rating matrix, but ignoring the ingredients which may have significant influence on users' preferences on items. In this paper, we propose a multi-linear interactive MF algorithm (MLIMF) to model the interactions between the users and each event associated with their final decisions. Our model considers not only the user-item rating information but also the pairwise interactions based on some empirically supported factors. In addition, we compared the proposed model with three typical other methods: user-based collaborative filtering (UCF), item-based collaborative filtering (ICF) and regularized MF (RMF). Experimental results on two real-world datasets, \emph{MovieLens} 1M and \emph{MovieLens} 100k, show that our method performs much better than other three methods in the accuracy of recommendation. This work may shed some light on the in-depth understanding of modeling user online behaviors and the consequent decisions.